A Basic Introduction to TensorFlow Lite

Light-weight: Edge devices have limited resources in terms of storage and computation capacity. Deep learning models are resource-intensive, so the models we deploy on edge devices should be light-weight with smaller binary sizes. As the inferences are made on the Edge device, a round trip from the device to the server will be eliminated, making inferences faster. Pre-trained: Models can be trained on-prem or cloud for different deep learning tasks like image classification, object detection, speech recognition, etc. and can be easily deployed to make inferences at the Edge. Light-weight: Edge devices have limited resources in terms of storage and computation capacity.